• 제목/요약/키워드: Approaches to Learning

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지식창출과 활용의 괴리: 녹색기술인증의 제도론적 분석 (KNOWLEDGE DECOUPLING: AN INSTITUTIONAL APPROACH TO THE GAP BETWEEN CREATION AND UTILIZATION OF ENVIRONMENTAL TECHNOLOGIES)

  • 박상찬;차현진
    • 지식경영연구
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    • 제18권1호
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    • pp.117-138
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    • 2017
  • While prior work has noted the importance of knowledge creation in gaining competitive advantages, much less is understood about why firms do not actually use what they create. Building upon institutional approaches to organization studies, we offer a new framework to explain the gap between knowledge creation and utilization. We test our framework in an empirical context of sustainable innovation and environmental technologies where ideas of environmental sustainability have recently gained public popularity and shaped how interested audiences make evaluative assessments of firms. In such a context, firms are apt to perceive the social attention toward sustainability to be a normative pressure, which causes them to create new knowledge and develop technologies consistent with the pressure. Using data from the government-initiated certification system for green technologies, our study finds that firms do not always fully implement new environmental technologies they develop in response to the certification program, the situation we refer to as knowledge decoupling. We also examine a set of conditions under which knowledge decoupling becomes more or less amplified. Taken together, our findings show how a firm's knowledge creation and utilization is shaped by its external institutional environment as well as internal learning processes.

지식내용, 사회문제, 개인흥미 중심의 통합과학교육 접근법을 적용한 '에너지' 주제의 교수.학습 방안 개발(II) (Three Teaching-Learning Plans for Integrated Science Teaching of 'Energy' Applying Knowledge-, Social Problem-, and Individual Interest-Centered Approaches)

  • 이미혜;손연아;;최돈형
    • 한국과학교육학회지
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    • 제21권2호
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    • pp.357-384
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    • 2001
  • 본 논문에서는 통합과학교육에 관한 이론적 방향과 실제적 교수 학습방안을 연계성 있게 제시하여 과학교사들의 통합과학교육에 관한 이해를 도움과 동시에 통합과학 수업 보조자료를 개발하여 실제수업에 활용할 수 있도록 하였다. 이를 위해 공통과학 교과내용 중 통합 과학적 성격이 특히 강한 에너지 단원을 대상으로 통합과학 교수 학습 방안을 모색하였는데, 이것은 이전 논문에서 구성한 '통합과학교육의 방향별 에너지 교수 학습 전략' 을 바탕으로 하고 여기에 각 방향별 특징에 적합한 수업 모형을 적용한 것이다. 즉, 지식내용중심의 통합은 물리, 화학, 생물, 지구과학의 지식을 통합하기 위해서 '에너지의 여행' 을 주제로 선정하고 ' 개방된 발견학습' 수업모형을 적용하여 개념과 탐구관련 중심으로 모색하였다. 사회문제중심의 통합은 과학관련 사회문제를 해결하기 위하여 '에너지의 미래'를 주제로 선정하고 '발생학습' 수업모형을 적용하여 학습자의 인지과정을 중심으로 모색하였다. 개인흥미중심의 통합은 과학과 개인흥미의 통합을 위하여 '에너지의 변신' 을 주제로 선정하고 '프로젝트' 수업모형을 적용하여 학습자의 흥미나 관심분야를 중심으로 모색하였다. 이상과 같은 방향에 따른 통합과학 교수 학습 방안은 다음과 같은 순서에 의해 모색되었다. 먼저, 각 주제별로 다루어야할 통합과학적 교수 학습 내용을 구성하고, 이를 바탕으로 각각의 주제를 통합적으로 수업하기 위한 통합과학적 수업 절차를 설계하였다. 그리고 작성한 수업 절차에 따라 실제 통합과학 수업에서 적용할 수 있는 통합과학적 수업 지도안을 작성하였다. 이상의 연구는 21세기를 대비한 통합과학교육의 방향정립과 교재, 교사, 학생을 고려한 종합적인 통합과학교육 프로그램 개발에 활용될 수 있을 것으로 생각된다.

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Flipped Learning: Strategies and Technologies in Higher Education

  • Miziuk, Viktoriia;Berdo, Rimma;Derkach, Larysa;Kanibolotska, Olha;Stadnii, Alla
    • International Journal of Computer Science & Network Security
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    • 제21권7호
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    • pp.63-69
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    • 2021
  • Flipped learning is necessary for modern education but quite difficult to implement. In pedagogical science, the question remains to what extent the practical work of the teacher in combination with the technologies of flipped learning will improve the quality of higher education. The aim of this article is to study the effectiveness and feasibility of using flipped learning technologies, assessing their perception by students (advantages and problems), identified an algorithm for introducing flipped learning technology in higher education institutions. Research methods. The main method is an experiment. An evaluation of the effectiveness of the study was conducted using a questionnaire and observation method. Statistical methods were used to evaluate the results of the experiment. The research hypothesis is that flipped learning allows the teacher to spend more time on an individual approach, to understand the real needs of students, and provide effective feedback, thereby improving the quality of learning and motivation of students, especially while studying complex material. The results of the study are to prove the effectiveness of the technology of flipped education in the study of complex disciplines, courses, topics. The use of flipped learning strategies improves the self-regulation of the educational process, group work skills, improves students' ability to learn, overcome difficulties. The technology of flipped learning in the presence of modern technical means and constant work on improving the level of digital literacy is an effective means for students to master complex topics and problematic issues that require additional consideration and discussion. The perspective of further research is the consideration of integrated approaches to the application of flipped learning technologies to the principles of STEAM-education, multilingual and multicultural programs, etc. It is also worth continuing to develop a set of methods aimed at enhancing the student's learning activities, the formation of group work skills, direct participation in creating the foundations of higher education.

앙상블 기법을 활용한 RNA-Sequencing 데이터의 폐암 예측 연구 (A Study on Predicting Lung Cancer Using RNA-Sequencing Data with Ensemble Learning)

  • Geon AN;JooYong PARK
    • Journal of Korea Artificial Intelligence Association
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    • 제2권1호
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    • pp.7-14
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    • 2024
  • In this paper, we explore the application of RNA-sequencing data and ensemble machine learning to predict lung cancer and treatment strategies for lung cancer, a leading cause of cancer mortality worldwide. The research utilizes Random Forest, XGBoost, and LightGBM models to analyze gene expression profiles from extensive datasets, aiming to enhance predictive accuracy for lung cancer prognosis. The methodology focuses on preprocessing RNA-seq data to standardize expression levels across samples and applying ensemble algorithms to maximize prediction stability and reduce model overfitting. Key findings indicate that ensemble models, especially XGBoost, substantially outperform traditional predictive models. Significant genetic markers such as ADGRF5 is identified as crucial for predicting lung cancer outcomes. In conclusion, ensemble learning using RNA-seq data proves highly effective in predicting lung cancer, suggesting a potential shift towards more precise and personalized treatment approaches. The results advocate for further integration of molecular and clinical data to refine diagnostic models and improve clinical outcomes, underscoring the critical role of advanced molecular diagnostics in enhancing patient survival rates and quality of life. This study lays the groundwork for future research in the application of RNA-sequencing data and ensemble machine learning techniques in clinical settings.

From Information to Knowledge: The Information Literacy Conundrum

  • Todd, Ross J.
    • 한국문헌정보학회지
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    • 제44권4호
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    • pp.131-153
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    • 2010
  • The fusion of learning, information, and technology presents dynamic challenges for all librarians, educators and students in 21st century libraries and schools. At the heart of this fusion is the growth of a pervasive, integrated information environment characterized by vast quantities of digital content, open choice, collaborative and participatory digital spaces, and the transition of the web environments from consumption of information to creation of information. This environment heralds important opportunities for librarians and teachers to rethink, re-imagine and recreate a dynamic approaches to information literacy instruction. Drawing on an extensive body of research undertaken through the Center for International Scholarship in School Libraries (CISSL), and published research on both information literacy and constructivist learning, this paper provides a critical examination of the current status of information literacy: its multiple conceptualizations, competing models, viewpoints, and its operationalizations in educational and library environments. The paper will challenge information literacy practices which center on simplistic, reductionist approaches to information literacy development, and the separation of information process and knowledge content. In particular it will address apparent contradictions in espoused conceptions of information literacy which revolve around "knowledge": knowledge construction, critical thinking, problem solving and the development of knowledgeable people; and information literacy practices which revolve around "information": a predominant focus on skills of access and evaluation of resources and with less attention given to engaging with found information to develop deep knowledge and understanding. The paper will present a series of challenges for moving forward with information literacy agendas in libraries and schools.

A Conceptual Framework for Determination of Appropriate Business Model in e-Learning Industry in Iran

  • Salehinejad, Abbas;Samizadeh, Reza
    • Asian Journal of Business Environment
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    • 제7권4호
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    • pp.17-25
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    • 2017
  • Purpose - The purpose of this study is to present a framework for determining the most appropriate business model for e-learning. Research design, data, and methodology - The Electronics Branch of Azad University has been elected as a case study in this research. This study conducted using a descriptive method. The information was obtained using interviews with experts including managers, faculty and students at the Electronics Branch of Azad University. Results - Three service-product system (product oriented system, use an oriented and result oriented system) approaches determined a framework for the formation of a portfolio. This portfolio is including three types of e-learning business models. Examining the relevant characteristics, correspondence of behaviorism learning theory with a product-oriented approach, correspondence of cognitivism theory with a user-oriented approach and in finally match correspondence of constructivist learning theory with a results-oriented approach which is evident. Conclusions - After reviewing the literature on the fields of e-learning, business model and product - service systems, we have achieved three types of e-learning business models. Then the variables in any of the business models were defined by using business model canvas tool and thus a portfolio consisting of three types of e-learning business model canvas was obtained.

Lightweight Named Entity Extraction for Korean Short Message Service Text

  • Seon, Choong-Nyoung;Yoo, Jin-Hwan;Kim, Hark-Soo;Kim, Ji-Hwan;Seo, Jung-Yun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제5권3호
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    • pp.560-574
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    • 2011
  • In this paper, we propose a hybrid method of Machine Learning (ML) algorithm and a rule-based algorithm to implement a lightweight Named Entity (NE) extraction system for Korean SMS text. NE extraction from Korean SMS text is a challenging theme due to the resource limitation on a mobile phone, corruptions in input text, need for extension to include personal information stored in a mobile phone, and sparsity of training data. The proposed hybrid method retaining the advantages of statistical ML and rule-based algorithms provides fully-automated procedures for the combination of ML approaches and their correction rules using a threshold-based soft decision function. The proposed method is applied to Korean SMS texts to extract person's names as well as location names which are key information in personal appointment management system. Our proposed system achieved 80.53% in F-measure in this domain, superior to those of the conventional ML approaches.

Abnormal state diagnosis model tolerant to noise in plant data

  • Shin, Ji Hyeon;Kim, Jae Min;Lee, Seung Jun
    • Nuclear Engineering and Technology
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    • 제53권4호
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    • pp.1181-1188
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    • 2021
  • When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep-learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants.

지식내용, 사회문제, 개인흥미 중심의 통합과학교육 접근법을 적용한 '에너지' 주제의 교수.학습 전략 모색(I) (Three Strategies for Integrated Science Teaching of "Energy" Applying Knowledge, Social Problem, and Individual Interest Centered Approaches)

  • 이미혜;손연아;;최돈형
    • 한국과학교육학회지
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    • 제21권2호
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    • pp.342-356
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    • 2001
  • 우리나라 제 6차 교육과정과 2002년부터 점차로 적용되는 제 7차 교육과정은 중등학교에서의 통합과학교육을 강조하고 있다. 그럼에도 불구하고 대부분의 중등학교 과학교사가 통합과학교과의 지도에 있어 애로를 겪고 있으며, 이는 실제적인 중등학교 통합과학교육의 심각한 문제점으로 대두되고 있다. 본 논문에서는 이러한 문제점의 원인을 일선과학 교사가 통합과학교육을 실제로 어떻게 실행해야하는지를 충분히 인식하지 못하고 있다는 점(손연아와 이학동, 1999) 과 실제 수업에서 교사가 활용할 수 있는 통합과학 교수-학습자료가 불충분하다는 점에서 찾았다. 실제 수업에서 통합과학교육 실행의 문제점을 해결하기 위해서 본 논문에서는 다음과 같은 통합과학교육의 3가지 방향을 적용하였다 : 지식내용 중심의 통합과학교육, 사회문제 중심의 통합과학교육, 개인흥미중심의 통합과학교육. 이상의 관점에 따라 본 논문에서는 통합과학교육의 3가지 방향에 대한 실제적 교수 학습 전략을 모색하기 위해서 다음의 2 단계로 연구를 진행하였다. 먼저, 한국의 제 6, 7차 과학교육과정과 7종의 공통과학 교과서, 그리고 미국의 NSES와 Benchmarks 국가 과학교육과정에 포함된 에너지 관련 내용을 분석하여 본 논문에서 공통적으로 적용할 4가지 에너지 관련 개념을 추출하였다; 에너지 보존, 에너지 전환, 에너지 흐름, 에너지 감성, 다음으로, 이상에서 분석한 에너지 관련 내용과 추출한 개념을 중심으로 3가지 통합과학교육의 방향을 기초로 한 3가지 '에너지' 주제의 교수 학습 개념 구조도를 구성하였다. 이러한 통합과학 교수 학습 전략은 통합의 방향별 에너지 주제의 통합과학교육 실행을 위해 후속 연구에서 제시될 통합과학 교수 학습 방안을 모색하는데 이론적 근거로 제공될 것이다.

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Breast Tumor Cell Nuclei Segmentation in Histopathology Images using EfficientUnet++ and Multi-organ Transfer Learning

  • Dinh, Tuan Le;Kwon, Seong-Geun;Lee, Suk-Hwan;Kwon, Ki-Ryong
    • 한국멀티미디어학회논문지
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    • 제24권8호
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    • pp.1000-1011
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    • 2021
  • In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.